arXiv Computer Vision
Sep 1

PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models

arXiv:2605.22311v2 Announce Type: replace Abstract: Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as...

By Jose Edgar Hernandez Cancino Estrada, Mauro D\'iaz Lupone, \v{Z}iga Emer\v{s}i\v{c}, Vitomir \v{S}truc, Peter Peer, Darian Toma\v{s}evi\'c
arXiv AI
Jun 3

PURGE: Projected Unlearning via Retain-Guided Erasure

arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.

By Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui, Prashant Trivedi, Yash Sinha, Pratik Narang
arXiv Computer Vision
Sep 24

Damnatio Memoriae: Adversarially and Selectively Forgetting Identities in the Embedding Space of Face Recognition Models

The paper introduces "open‑set adversarial forgetting" to make selected identities unlinkable in face recognition models while keeping the system functional for other users. Three loss functions are proposed: one disperses embeddings from their centroid, and two map each image to a near‑orthogonal target—either learned via the classifier head or fixed as an almost‑orthonormal frame. Experiments on multiple backbones and forget scales show that these losses, especially the orthonormal‑frame approach, effectively render the targeted identities unidentifiable and outperform prior methods while preserving higher retain rates.

By \"Unsal \"Ozt\"urk, Vedrana Krivoku\'ca Hahn, Sushil Bhattacharjee, S\'ebastien Marcel